New error measures and methods for realizing protein graphs from distance data

TL;DR

This paper introduces new error measures and compares various mathematical programming methods for reconstructing protein structures from interval distance data, concluding that a novel heuristic based on multiplicative weights updates performs best.

Contribution

It presents a new error measure tailored for protein backbones and evaluates multiple formulations and solvers, identifying the most effective approach.

Findings

The new error measure effectively captures protein backbone features.

The multiplicative weights update heuristic outperforms other methods.

Comprehensive computational evaluation guides best method selection.

Abstract

The interval Distance Geometry Problem (iDGP) consists in finding a realization in $\mathbb{R}^K$ of a simple undirected graph $G=(V,E)$ with nonnegative intervals assigned to the edges in such a way that, for each edge, the Euclidean distance between the realization of the adjacent vertices is within the edge interval bounds. In this paper, we focus on the application to the conformation of proteins in space, which is a basic step in determining protein function: given interval estimations of some of the inter-atomic distances, find their shape. Among different families of methods for accomplishing this task, we look at mathematical programming based methods, which are well suited for dealing with intervals. The basic question we want to answer is: what is the best such method for the problem? The most meaningful error measure for evaluating solution quality is the coordinate root mean square deviation. We first introduce a new error measure which addresses a particular feature of protein backbones, i.e. many partial reflections also yield acceptable backbones. We then present a set of new and existing quadratic and semidefinite programming formulations of this problem, and a set of new and existing methods for solving these formulations. Finally, we perform a computational evaluation of all the feasible solver$+$formulation combinations according to new and existing error measures, finding that the best methodology is a new heuristic method based on multiplicative weights updates.